DE-IE: differential evolution for color image enhancement
Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired some...
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| Autor*in: |
Kumar, Sushil [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Systems Assurance Engineering and Management - Springer-Verlag, 2010, 9(2014), 3 vom: 19. Juni, Seite 577-588 |
|---|---|
| Übergeordnetes Werk: |
volume:9 ; year:2014 ; number:3 ; day:19 ; month:06 ; pages:577-588 |
| Links: |
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| DOI / URN: |
10.1007/s13198-014-0278-6 |
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| 520 | |a Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. | ||
| 650 | 4 | |a Color image enhancement |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Differential evolution |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Histogram |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Homogeneity histogram |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Pant, Millie |4 aut | |
| 700 | 1 | |a Ray, Amiya Kumar |4 aut | |
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10.1007/s13198-014-0278-6 doi (DE-627)SPR031291414 (SPR)s13198-014-0278-6-e DE-627 ger DE-627 rakwb eng Kumar, Sushil verfasserin aut DE-IE: differential evolution for color image enhancement 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. Color image enhancement (dpeaa)DE-He213 Differential evolution (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Histogram (dpeaa)DE-He213 Homogeneity histogram (dpeaa)DE-He213 Pant, Millie aut Ray, Amiya Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2014), 3 vom: 19. Juni, Seite 577-588 (DE-627)SPR031222420 nnns volume:9 year:2014 number:3 day:19 month:06 pages:577-588 https://dx.doi.org/10.1007/s13198-014-0278-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2014 3 19 06 577-588 |
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10.1007/s13198-014-0278-6 doi (DE-627)SPR031291414 (SPR)s13198-014-0278-6-e DE-627 ger DE-627 rakwb eng Kumar, Sushil verfasserin aut DE-IE: differential evolution for color image enhancement 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. Color image enhancement (dpeaa)DE-He213 Differential evolution (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Histogram (dpeaa)DE-He213 Homogeneity histogram (dpeaa)DE-He213 Pant, Millie aut Ray, Amiya Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2014), 3 vom: 19. Juni, Seite 577-588 (DE-627)SPR031222420 nnns volume:9 year:2014 number:3 day:19 month:06 pages:577-588 https://dx.doi.org/10.1007/s13198-014-0278-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2014 3 19 06 577-588 |
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10.1007/s13198-014-0278-6 doi (DE-627)SPR031291414 (SPR)s13198-014-0278-6-e DE-627 ger DE-627 rakwb eng Kumar, Sushil verfasserin aut DE-IE: differential evolution for color image enhancement 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. Color image enhancement (dpeaa)DE-He213 Differential evolution (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Histogram (dpeaa)DE-He213 Homogeneity histogram (dpeaa)DE-He213 Pant, Millie aut Ray, Amiya Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2014), 3 vom: 19. Juni, Seite 577-588 (DE-627)SPR031222420 nnns volume:9 year:2014 number:3 day:19 month:06 pages:577-588 https://dx.doi.org/10.1007/s13198-014-0278-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2014 3 19 06 577-588 |
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10.1007/s13198-014-0278-6 doi (DE-627)SPR031291414 (SPR)s13198-014-0278-6-e DE-627 ger DE-627 rakwb eng Kumar, Sushil verfasserin aut DE-IE: differential evolution for color image enhancement 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. Color image enhancement (dpeaa)DE-He213 Differential evolution (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Histogram (dpeaa)DE-He213 Homogeneity histogram (dpeaa)DE-He213 Pant, Millie aut Ray, Amiya Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2014), 3 vom: 19. Juni, Seite 577-588 (DE-627)SPR031222420 nnns volume:9 year:2014 number:3 day:19 month:06 pages:577-588 https://dx.doi.org/10.1007/s13198-014-0278-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2014 3 19 06 577-588 |
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Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 |
| abstractGer |
Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 |
| abstract_unstemmed |
Abstract Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 |
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| title_short |
DE-IE: differential evolution for color image enhancement |
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https://dx.doi.org/10.1007/s13198-014-0278-6 |
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| author2 |
Pant, Millie Ray, Amiya Kumar |
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Pant, Millie Ray, Amiya Kumar |
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| doi_str |
10.1007/s13198-014-0278-6 |
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2024-07-03T23:01:49.915Z |
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